[UAI] CFP special session on "Learning with confidence"

From: Dario Malchiodi (malchiodi@dsi.unimi.it)
Date: Wed Jan 30 2002 - 14:22:13 PST

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    Many apologizes for cross-posting

    SCI2002
    Sixth World Multiconference
    on Systemics, Cybernetics and Informatics
    July 14-18, 2002 ~ Orlando, Florida

    Special Session: Learning with confidence
    http://laren.dsi.unimi.it/SCI2002/

    Call for papers

    Leaving the asymptotic learnability results of early sixties, for
    instance from E. Gold or A. Gill, modern theories consider learning as a
    statistical operation, possibly based on highly structured sample
    values, possibly done in a very poor probabilistic framework. In this
    scenario the target of our learning task is generally a function that is
    a random object, and we want to frame its variability within a set of
    possible realizations with satisfactory confidence. Under a
    computational perspective this problem reads in terms of sample
    complexity for a given accuracy (a relevant measure of the width of the
    realization set) and In the aim of locating the learning task in the one
    or other side of the exponential complexity divide, former results came
    from rather elementary probabilistic modeling based on binomial
    experiments and sharp bounds such as those coming from Chernoff
    inequality. Subsequent comparisons of the algorithms efficiency on a
    same learning task lead to the employment of more sophisticated
    statistical tools to identify very accurate confidence intervals, in
    relation with both sample properties - such as their distribution law or
    error rate - and structural constraints - such as the allowed complexity
    of the statistics. These theoretical improvements allow, for instance,
    to distinguish between different degrees of the
    polynomials describing sample complexities of algorithms for learning a
    monotone DNF under proper probability hypotheses on the example space.
    Many efforts have also been devoted to the confidence intervals for the
    shape of continuous functions, with results concerning trained neural
    networks as well.The session aims at collecting contributions by
    researchers involved in these topics. The special perspective is the
    exploitation of relations between the randomness of the training
    examples and their mutual dependence exactly denoted by the function we
    want discovering from them.

    Submissions

    A 2000 characters abstract should be submitted in electronic format
    (preferably in PDF, but PostScript or MS Word are also acceptable
    formats) to apolloni@dsi.unimi.it within February 23, 2002, using as
    subject-line "SCI2002 Special session submission". After notification of
    acceptance the authors will have to submit within April 5, 2002 an
    extended abstract not exceeding the length of six pages. Please do not
    send your papers to SCI2002 secretariat. All papers must be presented by
    one of the authors, who must pay the registration fee. For more
    information about the general conference please see
    http://www.iiisci.org/sci2002/.

    Session Chair
    Bruno Apolloni
    Dipartimento di Scienze dell'Informazione
    Universita' degli Studi di Milano
    Via Complico 39/41, I-20153 Milano - Italy
    Phone: +39 02 503 16284 Fax: +39 02 503 16288
    E-mail: apolloni@dsi.unimi.it
    confidence.



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